Point cloud registration (PCR) is crucial for many downstream tasks, such as simultaneous localization and mapping (SLAM) and object tracking. This makes detecting and quantifying registration misalignment, i.e., PCR quality validation, an important task. All existing methods treat validation as a classification task, aiming to assign the PCR quality to a few classes. In this work, we instead use regression for PCR validation, allowing for a more fine-grained quantification of the registration quality. We also extend previously used misalignment-related features by using multiscale extraction and attention-based aggregation. This leads to accurate and robust registration error estimation on diverse datasets, especially for point clouds with heterogeneous spatial densities. Furthermore, when used to guide a mapping downstream task, our method significantly improves the mapping quality for a given amount of re-registered frames, compared to the state-of-the-art classification-based method.

翻译：点云配准（PCR）对于许多下游任务（如同时定位与建图（SLAM）和物体跟踪）至关重要。这使得检测和量化配准未对准（即PCR质量验证）成为一项重要任务。所有现有方法都将验证视为分类任务，旨在将PCR质量划分为少数几个类别。在本工作中，我们转而使用回归进行PCR验证，从而允许对配准质量进行更细粒度的量化。我们还通过使用多尺度提取和基于注意力的聚合，扩展了先前使用的未对准相关特征。这导致在不同数据集上实现准确且稳健的配准误差估计，特别是对于具有异质空间密度的点云。此外，当用于指导建图下游任务时，与最先进的基于分类的方法相比，我们的方法在给定数量的重新配准帧下显著提高了建图质量。